A Comprehensive Robust Design Approach for Decision Trade-Offs in Complex Systems Design

1999 ◽  
Author(s):  
Monu Kalsi ◽  
Kurt Hacker ◽  
Kemper Lewis
Keyword(s):  
Robust Design ◽  
Systems Design ◽  
Decision Makers ◽  
Control Factor ◽  
Type I ◽  
Trade Offs ◽  
Design Variables ◽  
Complex Engineering ◽  
The Cost ◽  
Passenger Aircraft

Abstract In this paper we introduce a technique to reduce the effects of uncertainty and incorporate flexibility in the design of complex engineering systems involving multiple decision-makers. We focus on the uncertainty that is created when a disciplinary designer or design team must try to predict or model the behavior of other disciplinary subsystems. The design of a complex system is performed by many different designers and design teams, each of which may only have control over a portion of the total set of system design variables. Modeling the interaction among these decision-makers and reducing the effect caused by lack of global control by any one designer is the focus of this paper. We use concepts from robust design to reduce the effects of decisions made during the design of one subsystem on the performance of the rest of the system. Thus, in a situation where the cost of uncertainty is high, these tools can be used to increase the robustness, or independence, of the subsystems, enabling designers to make more effective decisions. This approach includes uncertainty caused by control factor variation (Type II robust design) and uncertainty caused by unknown nonlocal design information (Type I robust design). To demonstrate the usefulness of this approach, we consider a case study involving the design of a passenger aircraft.

A Comprehensive Robust Design Approach for Decision Trade-Offs in Complex Systems Design

1999 ◽  
Vol 123 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Monu Kalsi ◽  
Kurt Hacker ◽  
Kemper Lewis
Keyword(s):  
Robust Design ◽  
Systems Design ◽  
Decision Makers ◽  
Engineering Systems ◽  
Trade Offs ◽  
Design Variables ◽  
Complex Engineering ◽  
The Cost ◽  
Passenger Aircraft

In this paper we introduce a technique to reduce the effects of uncertainty and incorporate flexibility in the design of complex engineering systems involving multiple decision-makers. We focus on the uncertainty that is created when a disciplinary designer or design team must try to predict or model the behavior of other disciplinary subsystems. The design of a complex system is performed by many different designers and design teams, each of which may only have control over a portion of the total set of system design variables. Modeling the interaction among these decision-makers and reducing the effect caused by lack of global control by any one designer is the focus of this paper. We use concepts from robust design to reduce the effects of decisions made during the design of one subsystem on the performance of the rest of the system. Thus, in a situation where the cost of uncertainty is high, these tools can be used to increase the robustness, or independence, of the subsystems, enabling designers to make more effective decisions. To demonstrate the usefulness of this approach, we consider a case study involving the design of a passenger aircraft.

Using Robust Design Techniques to Model the Effects of Multiple Decision Makers in a Design Process

1998 ◽  
Author(s):  
Kurt Hacker ◽  
Kemper Lewis
Keyword(s):  
Robust Design ◽  
Interaction Analysis ◽  
System Modeling ◽  
Decision Makers ◽  
Engineering System ◽  
Entire System ◽  
Multiple Decision Makers ◽  
Complex Engineering ◽  
Passenger Aircraft ◽  
Complex Engineering System

Abstract In this paper we introduce a methodology to reduce the effects of uncertainty in the design of a complex engineering system involving multiple decision makers. We focus on the uncertainty that is created when a disciplinary designer or design team must try and predict or model the behavior of other disciplinary subsystems. The design of a complex system is performed by many different designers and teams, each of which only have control over a small portion of the entire system. Modeling the interaction among these decision makers and reducing the uncertainty caused by the lack of global control is the focus of this paper. We use well developed concepts from the field of game theory to describe the interactions taking place, and concepts from robust design to reduce the effects of one decision-maker on another. Response Surface Methodology (RSM) is also used to reduce the complexity of the interaction analysis while preserving behavior of the systems. The design of a passenger aircraft is used to illustrate the approach, and some encouraging results are discussed.

A Procedure for Robust Design: Minimizing Variations Caused by Noise Factors and Control Factors

1996 ◽  
Vol 118 (4) ◽  
pp. 478-485 ◽  
Author(s):  
Wei Chen ◽  
J. K. Allen ◽  
Kwok-Leung Tsui ◽  
F. Mistree
Keyword(s):  
Robust Design ◽  
Irrigation System ◽  
Small Variation ◽  
Type I ◽  
Closed Form Solutions ◽  
Control Factors ◽  
Design Variables ◽  
Solar Powered ◽  
And Control ◽  
Noise Factors

In this paper, we introduce a small variation to current approaches broadly called Taguchi Robust Design Methods. In these methods, there are two broad categories of problems associated with simultaneously minimizing performance variations and bringing the mean on target, namely, Type I—minimizing variations in performance caused by variations in noise factors (uncontrollable parameters). Type II—minimizing variations in performance caused by variations in control factors (design variables). In this paper, we introduce a variation to the existing approaches to solve both types of problems. This variation embodies the integration of the Response Surface Methodology (RSM) with the compromise Decision Support Problem (DSP). Our approach is especially useful for design problems where there are no closed-form solutions and system performance is computationally expensive to evaluate. The design of a solar powered irrigation system is used as an example.

scholarly journals Moral Choice When Harming Is Unavoidable

2020 ◽  
Vol 31 (10) ◽  
pp. 1294-1301
Author(s):  
Jonathan Z. Berman ◽  
Daniella Kupor
Keyword(s):  
Social Welfare ◽  
Negative Impact ◽  
Past Research ◽  
Harm Avoidance ◽  
Decision Makers ◽  
Moral Choice ◽  
Trade Offs ◽  
The Cost ◽  
Do So

Past research suggests that actors often seek to minimize harm at the cost of maximizing social welfare. However, this prior research has confounded a desire to minimize the negative impact caused by one’s actions (harm aversion) with a desire to avoid causing any harm whatsoever (harm avoidance). Across six studies ( N = 2,152), we demonstrate that these two motives are distinct. When decision-makers can completely avoid committing a harmful act, they strongly prefer to do so. However, harming cannot always be avoided. Often, decision-makers must choose between committing less harm for less benefit and committing more harm for more benefit. In these cases, harm aversion diminishes substantially, and decision-makers become increasingly willing to commit greater harm to obtain greater benefits. Thus, value trade-offs that decision-makers refuse to accept when it is possible to completely avoid committing harm can suddenly become desirable when some harm must be committed.

A Distributed Pool Architecture for Highly Constrained Optimization Problems in Complex Systems Design

2013 ◽  
Vol 13 (3) ◽  
Author(s):  
Vijitashwa Pandey ◽  
Zissimos P. Mourelatos
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Solution Process ◽  
Systems Design ◽  
Mixed Integer ◽  
Engineering Systems ◽  
Design Problems ◽  
Design Variables ◽  
Complex Engineering

Optimal design of complex engineering systems is challenging because numerous design variables and constraints are present. Dynamic changes in design requirements and lack of complete knowledge of subsystem requirements add to the complexity. We propose an enhanced distributed pool architecture to aid distributed solving of design optimization problems. The approach not only saves solution time but is also resilient against failures of some processors. It is best suited to handle highly constrained design problems, with dynamically changing constraints, where finding even a feasible solution (FS) is challenging. In our work, this task is distributed among many processors. Constraints can be easily added or removed without having to restart the solution process. We demonstrate the efficacy of our method in terms of computational savings and resistance to partial failures of some processors, using two mixed integer nonlinear programming (MINLP)-class mechanical design optimization problems.

scholarly journals Capacity Decisions for an Emergency Service

1978 ◽  
Vol 10 (2) ◽  
pp. 121-123
Author(s):  
James W. Dunn ◽  
Gerald A. Doeksen
Keyword(s):  
Law Enforcement ◽  
Fire Protection ◽  
Emergency Service ◽  
Emergency Services ◽  
Decision Makers ◽  
Quality Of Services ◽  
Capacity Decisions ◽  
Trade Offs ◽  
The Cost

Decision makers face two opposing forces in the provision of emergency services. Their constituency wants more and better services, but financial considerations limit the quantity and quality of services provided. This classic economic confrontation requires a decision based on the trade-offs between the benefits of protection provided by additional services and the cost of providing these services. Such a decision is needed for ambulance service, fire protection, and law enforcement.

scholarly journals Stackelberg game approach to a bi-objective robust design optimization

2021 ◽  
Vol 37 (3) ◽  
Author(s):  
L. Dai ◽  
M. Tang ◽  
S. Shin
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Optimization Problems ◽  
Stackelberg Game ◽  
Design Procedure ◽  
Primary Objective ◽  
Robust Design Optimization ◽  
Control Factor ◽  
Trade Offs ◽  
Dual Response

Robust design has received a great deal of attention from quality researchers in recent years, and a number of optimization methodologies based on the dual response format have been proposed. The majority of existing bi-objective optimization models concentrate on the trade-offs between the process mean and variability functions without investigating the interactions between control factors and quality characteristics. The primary objective of this research is to integrate the Stackelberg leadership model into the robust design procedure and propose a Stackelberg game-based robust design (SGRD) method to determine appropriate control factor settings by minimizing the values of desired optimization targets based on an analysis of possible combinations of input and output quality parameters. Herein, first, a bi-objective robust design optimization problem is formulated as a dual response model using response surface methodology (RSM). Second, the proposed SGRD model is developed via decomposition into two leader-follower game models. Finally, the mean square error (MSE) criterion is applied to evaluate models, and select non-dominated solutions in various situations. Numerical examples are used to demonstrate that the proposed method provides significant solutions in cases containing unidentified priorities between the dual responses and undiscovered correlations among several inputs and outcomes. In addition, according to the case study analysis, the proposed method is more efficient than the conventional dual response approach when dealing with bi-objective robust design optimization problems.

scholarly journals A method for robust design in a coupled decision environment

2021 ◽  
Vol 7 ◽  
Author(s):  
Gehendra Sharma ◽  
Janet K. Allen ◽  
Farrokh Mistree
Keyword(s):  
Robust Design ◽  
Type I ◽  
Type Ii ◽  
Design Decisions ◽  
Design Variables ◽  
Robust Decisions ◽  
Decision Environment ◽  
Function Relationship ◽  
Noise Factors ◽  
Selection Of

Abstract The design of a connected engineered system requires numerous design decisions that influence one another. In a connected system that comprises numerous interacting decisions involving concurrency and hierarchy, accounting for interactions while also managing uncertainties, it is imperative to make robust decisions. In this article, we present a method for robust design using coupled decisions to identify design decisions that are relatively insensitive to uncertainties. To account for the influence among decisions, design decisions are modelled as coupled decisions. They are defined using three criteria: the types of decisions, the strength of interactions and the decision levels. In order to make robust decisions, robust design methods are classified based on sources of uncertainty, namely, Type I (noise factors), Type II (design variables) and Type III (function relationship between design variables and responses). The design of a one-stage reduction gearbox is used as a demonstration example. To illustrate the proposed method for robust design using coupled decisions, we present the simultaneous selection of gear material and gearbox geometry in a coupled decision environment while managing the uncertainties involved in designing gearboxes.

Robust Design for Multiscale and Multidisciplinary Applications

2006 ◽  
Vol 128 (4) ◽  
pp. 832-843 ◽  
Author(s):  
Janet K. Allen ◽  
Carolyn Seepersad ◽  
HaeJin Choi ◽  
Farrokh Mistree
Keyword(s):  
Robust Design ◽  
Type I ◽  
Uncertain Information ◽  
Control Factors ◽  
Multiscale Systems ◽  
Design Variables ◽  
Challenges And Opportunities ◽  
Quality Of Products ◽  
Many Sources ◽  
Noise Factors

The intent in robust design is to improve the quality of products and processes by reducing their sensitivity to variations, thereby reducing the effects of variability without removing its sources. Robust design is especially useful for integrating information from designers working at multiple length and time scales. Inevitably this involves the integration of uncertain information. This uncertainty is derived from many sources and robust design may be classified based on these sources—uncertainty in noise or environmental and other noise factors (type I); uncertainty in design variables or control factors (type II); and uncertainty introduce by modeling methods (type III). Each of these types of uncertainty can be mitigated by robust design. Of particular interest are the challenges associated with the design of multidisciplinary and multiscale systems; these challenges and opportunities are examined in the context of materials design.

The Effects of Decision Time on Perceptions of Decisions and Decision Makers in (Moral) Trade-Off Scenarios

2019 ◽  
Author(s):  
Anna Katharina Spälti ◽  
Mark John Brandt ◽  
Marcel Zeelenberg
Keyword(s):  
Decision Process ◽  
Decision Time ◽  
Decision Makers ◽  
Process Information ◽  
Total N ◽  
Trade Off ◽  
Financial Gain ◽  
Trade Offs ◽  
Sacred Values ◽  
Moral Context

People often have to make trade-offs. We study three types of trade-offs: 1) "secular trade-offs" where no moral or sacred values are at stake, 2) "taboo trade-offs" where sacred values are pitted against financial gain, and 3) "tragic trade-offs" where sacred values are pitted against other sacred values. Previous research (Critcher et al., 2011; Tetlock et al., 2000) demonstrated that tragic and taboo trade-offs are not only evaluated by their outcomes, but are also evaluated based on the time it took to make the choice. We investigate two outstanding questions: 1) whether the effect of decision time differs for evaluations of decisions compared to decision makers and 2) whether moral contexts are unique in their ability to influence character evaluations through decision process information. In two experiments (total N = 1434) we find that decision time affects character evaluations, but not evaluations of the decision itself. There were no significant differences between tragic trade-offs and secular trade-offs, suggesting that the decisions structure may be more important in evaluations than moral context. Additionally, the magnitude of the effect of decision time shows us that decision time, may be of less practical use than expected. We thus urge, to take a closer examination of the processes underlying decision time and its perception.

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